Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This work shows that the relative content of two histological abnormality sensitive bands in tumor cells is significantly different from that of normal cells, and can be a biomarker to classify these cells, which will indicate the decision tree to be the primary algorithm in tumor-cell classification.
14 citations
••
TL;DR: This paper proposes the first secure protocol for collaborative evaluation of random forests contributed by multiple owners that outsource evaluation tasks to a third-party evaluator and is based on the new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption.
Abstract: Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client's encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.
14 citations
•
01 Feb 2019
TL;DR: In this paper, a federated learning method for multi-party data was proposed, in which the data terminal performs federation training on the multiparty training samples based on the gradient descent tree GBDT algorithm to construct a gradient tree model.
Abstract: The invention discloses a federation learning method, a system and a readable storage medium. The federated learning method includes the following steps: the data terminal performs federation trainingon the multi-party training samples based on the gradient descent tree GBDT algorithm, to construct a gradient tree model, wherein the data terminal is a plurality of, the gradient tree model comprises a plurality of regression trees, the regression trees comprise a plurality of partition points, and the training sample comprises a plurality of features, the features correspond to the partition points one by one; the data terminal performs joint prediction on a sample to be predicted based on the gradient tree model to determine a prediction value of the sample to be predicted. The inventioncarries out federation training on multi-party training samples through GBDT algorithm, realizes the establishment of gradient tree model, and is suitable for scenes with large data volume and can well meet the needs of realistic production environment through the gradient tree model. Forecast the sample to be forecasted jointly, and realize the forecast of the sample to be forecasted.
14 citations
••
TL;DR: A model of neural tree architecture with probabilistic neurons used for classification of a large amount of computer grid resources to classes is proposed and improvements have been made even for middle and small batch of tasks.
Abstract: This paper proposes a model of neural tree architecture with probabilistic neurons. These trees are used for classification of a large amount of computer grid resources to classes. The first tree is used for classification of hardware part of dataset. The second tree classifies patterns of software identifiers. Trees are implemented to successfully separate inputs into nine classes of resources. We propose Particle Swarm Optimization model for tasks scheduling in computer grid. We compared time of creation of schedule and time of makespan in six series of experiments without and with using neural trees. In experiments with using neural tree we gained the subset of suitable computational resources. The aim is effective mapping of a large batch of tasks into particular resources. On the base of experiments we can say that improvements have been made even for middle and small batch of tasks.
14 citations
••
TL;DR: A personalized approach in which a new type of decision tree model called decision-path model is constructed that takes advantage of the particular features of a given person of interest is described and evaluated.
Abstract: Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.
14 citations